Comparative Analysis of Classification Algorithms on Different Datasets using WEKA

Data mining is the upcoming research area to solve various problems and classification is one of main problem in the field of data mining. In this paper, we use two classification algorithms J48 (which is java implementation of C4.5 algorithm) and multilayer perceptron alias MLP (which is a modification of the standard linear perceptron) of the Weka interface. It can be used for testing several datasets. The performance of J48 and Multilayer Perceptron have been analysed so as to choose the better algorithm based on the conditions of the datasets. The datasets have been chosen from UCI Machine Learning Repository. Algorithm J48 is based on C4.5 decision based learning and algorithm Multilayer Perceptron uses the multilayer feed forward neural network approach for classification of datasets. When comparing the performance of both algorithms we found Multilayer Perceptron is better algorithm in most of the cases.